In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

The purpose of this study was to explore the biomechanical effects of progressive marginal bone loss in the peri-implant bone. Finite element model of a Ø 4.1 × 10 mm Straumann dental implant and a solid abutment was constructed as predefined eight-layers around the implant neck. The implant-abutment complex was embedded in a cylindrical bone model to analyze bone biomechanics regardless of anatomical influences. Angular and circular progressive marginal bone loss was simulated by sequential removal of each layer, resulting crater-like defects and a total of ten finite element models for analysis. Each model was subjected to a vertical and oblique static load of 100 N in separate load cases. Principal stress minimum and maximum, displacement, and equivalent of elastic strain outcomes were compared. Under vertical loading, principal stresses minimum and maximum decreased remarkably as with the increase in bone resorption. Under oblique load simulations, decrease in principal stress maximum and minimum was evident. With progressive bone loss and under oblique load simulations, displacement and equivalent of elastic strain increased considerably in trabecular bone contacting the implant neck. The presence of cortical bone contacting a load-carrying implant, even in a bone defect, improves the biomechanical performance of implants in comparison with only trabecular bone support as a sequel of progressive marginal bone loss.

Applanation resonance tonometry (ART) has been shown in a number of studies to be useful for measuring intraocular pressure (IOP). Data from in vitro laboratory bench testing, where the sensor was carefully centralised onto the cornea, has been very consistent with good precision in the determination of IOP. However, in a clinical study the unavoidable off-centre placement of the sensor against the cornea resulted in a reduced precision. The aim of this study was to evaluate a new design of the sensor with a symmetric sensor probe and a contact piece with a larger diameter. Two in vitro porcine eye experimental set-ups were used. One bench-based for examining position dependence and one biomicroscope-based set-up, simulating a clinical setting, for evaluating IOPART precision at seven different pressure levels (10–40 mmHg), set by connecting a saline column to the vitreous chamber. The reference IOP was recorded using a pressure transducer. There was no significant difference between four positions 1 mm off centre and the one centre position. The precision of the ART as compared with the reference pressure was ±1.03 mmHg (SD, n=42). The design improvement has enhanced the precision of the ART in the biomicroscope set-up to be in parity with bench test results from a set-up using perfect positioning. This indicates that off-centre positioning was no longer a major contributor to the deviations in measured IOP. The precision was well within the limits set by ISO standard for eye tonometers. Therefore, a larger in vivo study on human eyes with the ART should be performed.

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.

Spinal stenosis can be found in any part of the spine, though it is most commonly located on the lumbar and cervical areas. It has been documented in the literature that bilateral facetectomy in a lumbar motion segment to increase the space induces an increase in flexibility at the level at which the surgery was performed. However, the result of bilateral facetectomy on the stability of the thoracolumbar spine has not been studied. A nonlinear three-dimensional finite element (FE) model of thoracolumbar T11–L1 was built to explore the influence of bilateral facetectomy. The FE model of T11–L1 was validated against published experimental results under various physiological loadings. The FE model with bilateral facetectomy was evaluated under flexion, extension, lateral bending and axial rotation to determine alterations in kinematics. Results show that bilateral facetectomy causes increase in motion, considerable increase in axial rotation and least increase in lateral bending. Removal of facets did not result in significant change in the sagittal motion in flexion and extension.

We present an inertial sensor based monitoring system for measuring upper limb movements in real time. The purpose of this study is to develop a motion tracking device that can be integrated within a home-based rehabilitation system for stroke patients. Human upper limbs are represented by a kinematic chain in which there are four joint variables to be considered: three for the shoulder joint and one for the elbow joint. Kinematic models are built to estimate upper limb motion in 3-D, based on the inertial measurements of the wrist motion. An efficient simulated annealing optimisation method is proposed to reduce errors in estimates. Experimental results demonstrate the proposed system has less than 5% errors in most motion manners, compared to a standard motion tracker.

Geometric measures (volume, area and length) of biological particles are of fundamental interest for biological studies. Many times, the measures are at micro-/nano-scale, and based on images of the biological particles. This paper proposes a computational method to geometric measure of biological particles. The method has been applied to DNA microarray spot size estimation. Compared with existing algorithms for microarray spot size estimation, the proposed method is computational efficient and also provides confidence probability on the measure. The contributions of this paper include a generic computational method to geometric measure of biological particles and application to DNA microarray spot size estimation.

The present work seeks to determine if a particular non-linear analytic method is effective at quantifying uterine electromyography (EMG) data for estimating the onset of labor. Twenty-seven patients were included, and their uterine EMG was recorded non-invasively for 30 min. The patients were grouped into two sets: G1: labor, N=14; G2: antepartum, N=13. G1 patients all delivered spontaneously within 24 h of recording while G2 patients did not. The uterine electrical signals were analyzed offline by first isolating the uterine-specific frequency range and then randomly selecting “bursts” of uterine electrical activity (each associated with a uterine contraction) from every recording. Wavelet transform was subsequently applied to each of the bursts’ traces, and then the fractal dimension (FD) of the resulting transformed EMG burst-trace was calculated (Benoit 1.3, Trusoft). Average burst FD was found for each patient. FD means for G1 and G2 were calculated and compared using t test. FD was significantly higher (P<0.05) for G1: 1.27±0.03 versus G2: 1.25±0.02. The wavelet-decomposition-generated fractal dimension can be used to successfully discern between patients who will deliver spontaneously within 24 h and those who will not, and can be useful for the objective classification of antepartum versus labor patients.

Customizing the parameter values of brain interface (BI) systems by a human expert has the advantage of being fast and computationally efficient. However, as the number of users and EEG channels grows, this process becomes increasingly time consuming and exhausting. Manual customization also introduces inaccuracies in the estimation of the parameter values. In this paper, the performance of a self-paced BI system whose design parameter values were automatically user customized using a genetic algorithm (GA) is studied. The GA automatically estimates the shapes of movement-related potentials (MRPs), whose features are then extracted to drive the BI. Offline analysis of the data of eight subjects revealed that automatic user customization improved the true positive (TP) rate of the system by an average of 6.68% over that whose customization was carried out by a human expert, i.e., by visually inspecting the MRP templates. On average, the best improvement in the TP rate (an average of 9.82%) was achieved for four individuals with spinal cord injury. In this case, the visual estimation of the parameter values of the MRP templates was very difficult because of the highly noisy nature of the EEG signals. For four able-bodied subjects, for which the MRP templates were less noisy, the automatic user customization led to an average improvement of 3.58% in the TP rate. The results also show that the inter-subject variability of the TP rate is also reduced compared to the case when user customization is carried out by a human expert. These findings provide some primary evidence that automatic user customization leads to beneficial results in the design of a self-paced BI for individuals with spinal cord injury.

This paper deals with breast cancer diagnostic and prognostic estimations employing neural networks over the Wisconsin Breast Cancer datasets, which consist of measurements taken from breast cancer microscopic instances. A probabilistic approach is dedicated to solve the diagnosis problem, detecting malignancy among instances derived from the Fine Needle Aspirate test, while regression algorithms estimate the time interval that possibly correspond to the right end-point of the patients’ disease-free survival time or the time where the tumour recurs (time-to-recur). For the diagnosis problem, the accuracy of the neural network in terms of sensitivity and specificity was measured at 98.6 and 97.5% respectively, using the leave-one-out test method. As far as the prognosis problem is concerned, the accuracy of the neural network was measured through a stratified tenfold cross-validation approach. Sensitivity ranged between 80.5 and 91.8%, while specificity ranged between 91.9 and 97.9%, depending on the tested fold and the partition of the predicted period. The prognostic recurrence predictions were then further evaluated using survival analysis and compared with other techniques found in literature.

This work presents a study on the influence of the aqueous environment on the surface EMG (sEMG) signal recorded in bipolar montage from the abductor pollicis brevis muscle, when only the forearm is immersed in water. Ten men, 30.1±4.0 (mean ± SD) years old, performed ten 2-s 40% MVC isometric contractions of the abductor pollicis brevis muscle in two controlled environments (air and water, at a temperature of 32°C). They were always equipped with electrodes protected with a waterproof adhesive tape. No significant variations (paired Wilcoxon test) due to the environments were observed in the median frequency of the power spectrum (MDF) and in the root mean square (RMS) value of the sEMG signal. These results allow us to assess the methodological criteria to properly record sEMG signals in water and provide the basis to explain different findings obtained by other authors.

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We define a novel feature, Normalized Vector Separation (γij), to measure the separation of two arbitrary states “i” and “j” in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that γij can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via γij, with small NNs having no more than 21 connection weight altogether.

Bioelectrical fetal heart activity being recorded from maternal abdominal surface contains more information than mechanical heart activity measurement based on the Doppler ultrasound signals. However, it requires extraction of fetal electrocardiogram from abdominal signals where the maternal electrocardiogram is dominant. The simplest technique for maternal component suppression is a blanking procedure, which relies upon the replacement of maternal QRS complexes by isoline values. Although, in case of coincidence of fetal and maternal QRS complexes, it causes a loss of information on fetal heart activity. Its influence on determination of fetal heart rate and the variability analysis depends on the sensitivity of the heart-beat detector used. The sensitivity is defined as an ability to detect the incomplete fetal QRS complex. The aim of this work was to evaluate the influence of the maternal electrocardiogram suppression method used on the reliability of FHR signal being calculated.

We address the problem of prototypical waveform extraction in cognitive experiments using functional near-infrared spectroscopy (fNIRS) signals. These waveform responses are evoked with visual stimuli provided in an oddball type experimental protocol. As the statistical signal-processing tool, we consider the linear signal space representation paradigm and use independent component analysis (ICA). The assumptions underlying ICA is discussed in the light of the signal measurement and generation mechanisms in the brain. The ICA-based waveform extraction is validated based both on its conformance to the parametric brain hemodynamic response (BHR) model and to the coherent averaging technique. We assess the intra-subject and inter-subject waveform and parameter variability.

Cardiotocography is the most diffused prenatal diagnostic technique in clinical routine. The simultaneous recording of foetal heart rate (FHR) and uterine contractions (UC) provides useful information about foetal well-being during pregnancy and labour. However, foetal electronic monitoring interpretation still lacks reproducibility and objectivity. New methods of interpretation and new parameters can further support physicians’ decisions. Besides common time-domain analysis, study of the variability of FHR can potentially reveal autonomic nervous system activity of the foetus. In particular, it is clinically relevant to investigate foetal reactions to UC to diagnose foetal distress early. Uterine contraction being a strong stimulus for the foetus and its autonomic nervous system, it is worth exploring the FHR variability response. This study aims to analyse modifications of the power spectrum of FHR variability corresponding to UC. Cardiotocographic signal tracts corresponding to 127 UC relative to 30 healthy foetuses were analysed. Results mainly show a general, statistically significant (t test, p<0.01) power increase of the FHR variability in the LF 0.03–0.2 Hz and HF 0.2–1 in correspondence of the contraction with respect to a reference tract set before contraction onset. Time evolution of the power within these bands was computed by means of time-varying spectral estimation to concisely show the FHR response along a uterine contraction. A synchronised grand average of these responses was also computed to verify repeatability, using the contraction apex as time reference. Such modifications of the foetal HRV that follow a contraction can be a sign of ANS reaction and, therefore, additional, objective information about foetal reactivity during labour.

In this paper, we propose a downlink power-control mechanism to be applied in a multi-code code division multiple access (CDMA) mobile medicine system. The mobile medicine system can provide (i) measured blood pressure and body temperature, (ii) medical signals measured by the electrocardiogram (ECG) device, (iii) mobile patient’s history, (iv) G.729 audio signals, (v) Joint Photographic Experts Group 2000 Medical images and Moving Picture Experts Group 4 charge-coupled device sensor video signals. By utilizing a multi-code CDMA spread spectrum communication system with downlink power-control strategy, it is possible for this system to meet the quality of service requirements of a mobile medicine network. In addition, such a system can maximize the resource utilization. For different messages to be sent, the power is controlled according to the requisite bit error rate (BER). Higher transmission power is given to the media with lower BER requirement. Numerical analysis shows that the ratios of transmission power for voice, video, and data virtual channels is approximately 1:2:13 when the BERs for voice, video, and data are 10∧(− 3), 10∧(− 4), and 10∧(− 7), respectively. This power ratio is similar to the ratio of signal-to-noise plus interference power ratio for voice, video, and data during transmission. For the purpose of verifying the proposed argument, a simulation has been done and the results match the derivation very well.

Very few finite element models on the lumbosacral spine have been reported because of its unique biomechanical characteristics. In addition, most of these lumbosacral spine models have been only validated with rotation at single moment values, ignoring the inherent nonlinear nature of the moment–rotation response of the spine. Because a majority of lumbar spine surgeries are performed between L4 and S1 levels, and the confidence in the stress analysis output depends on the model validation, the objective of the present study was to develop a unique finite element model of the lumbosacral junction. The clinically applicable model was validated throughout the entire nonlinear range. It was developed using computed tomography scans, subjected to flexion and extension, and left and right lateral bending loads, and quantitatively validated with cumulative variance analyses. Validation results for each loading mode and for each motion segment (L4-L5, L5-S1) and bisegment (L4-S1) are presented in the paper.

An adaptive formulation of the long-term bi-directional linear predictive analysis is proposed in the context of the acoustic assessment of disordered speech. Vocal dysperiodicities are summarized by means of a signal-to-dysperiodicity ratio (SDR) marker. It is shown that performing an adaptive forward and backward long-term linear prediction of each speech sample and retaining the minimal prediction error energy as a cue of vocal dysperiodicity results in an SDR that correlates with the perceived degree of hoarseness. The coefficients of the time-varying long-term linear predictive model are estimated by means of the recursive least squares algorithm. The corpora comprise sustained vowels and French sentences produced by male and female normophonic and dysphonic speakers. A perceptual assessment of speech samples, which rests on comparative judgments, is used to evaluate the ability of the acoustic marker to predict subjective measures of voice quality. Experimental results show that the adaptive approach gives rise to high correlations for sustained vowels as well as for sentences.

One challenge in the current research of brain–computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.

Vagus nerve stimulation (VNS) is used in pharmaco-resistant epilepsy to decrease the number of seizures. Although it is well known that VNS affects respiration, there are only a few reports concerning an effect of VNS on heart rate or heart rate variability (HRV). We investigated the relationship between respiratory frequency and the high frequency (HF) domain of the discrete Fourier transform (DFT) of the RR interval function during night sleep recordings of ten subjects treated with VNS. Our results show that VNS shifts the frequency of maximal power spectrum density (PSD) in the HF-band, decreases the related PSD and induces a partial cardiorespiratory decoupling.